BUG: groupby over a CategoricalIndex in axis=1

[ekisslinger]

Code Sample

from pandas import DataFrame, CategoricalIndex

cat_index = CategoricalIndex(['a', 'b', 'a', 'b'], categories=['a', 'b'])
df = DataFrame(data=1.0, index=[0, 1], columns=cat_index)
print(df.groupby(axis=1, level=0).sum())
# Attempting a groupby using a CategoricalIndex results in:
#     ValueError: Categorical dtype grouper must have len(grouper) == len(data)

Problem description

Attempting a groupby over a CategoricalIndex for the columns results in a ValueError when using Pandas 0.21.0

Expected Output

     a    b
0  2.0  2.0
1  2.0  2.0

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.4.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-696.10.3.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US LOCALE: en_US.ISO8859-1 pandas: 0.21.0 pytest: None pip: 9.0.1 setuptools: 36.7.2 Cython: None numpy: 1.13.3 scipy: 1.0.0 pyarrow: 0.7.1 xarray: None IPython: 6.2.1 sphinx: 1.6.5 patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.3 blosc: None bottleneck: 1.2.1 tables: 3.4.2 numexpr: 2.6.4 feather: None matplotlib: 2.0.2 openpyxl: 2.5.0b1 xlrd: 1.0.0 xlwt: 1.3.0 xlsxwriter: None lxml: None bs4: None html5lib: 0.999999999 sqlalchemy: 1.1.6 pymysql: None psycopg2: None jinja2: 2.9.6 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

[jreback]

This works on the transpose.

In [60]: df.T.groupby(level=0).sum()
Out[60]: 
     0    1
a  2.0  2.0
b  2.0  2.0

a pull-request to fix would be great @ekisslinger